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How could I go about finding the weights or importance of inputs based on outputs?
How do “intent recognisers” work?Why does my Multilayer Perceptron only classify linearly?Are there established good algorithms for incremental feature learning for a neural network? Do any python ML libraries implement such algorithms?Is it possible to train a neural network to solve polynomial equations?Convolution Neural Network Loss and performanceNeural network q learning for tic tac toe - how to use the thresholdTitanic Kaggle Data: Why am I getting lower accuracy on Kaggle submissions than on held-out data?Navigating the jungle of choices for scalable ML deploymentFinding weights of independent features with an artificial neural network?Can I use an array as a model feature?
$begingroup$
I have a table who's inputs (sfm, fr, and doc) all affect the outputs (mmr and ra). How could I go about finding the input importance on the outputs? Basically, I'd like to be able to have a goal output in mmr and ra and have a good idea of starting parameters for sfm, fr, and doc. Does anyone have insight into something like this? Below is a sample of the data.
sfm fr doc mmr ra
60 0.15 0.1 449.6 1.85
60 0.15 0.2 896.78 0.86
60 0.15 0.25 1116.34 1.28
60 0.2 0.1 593.46 1.42
60 0.2 0.2 1183.62 0.91
60 0.2 0.25 1473.34 1.91
60 0.25 0.1 734.26 1.59
60 0.25 0.2 1464.41 1.52
60 0.25 0.25 1822.79 1.07
70 0.15 0.1 503.3 1.42
70 0.15 0.2 1003.74 0.89
70 0.15 0.25 750.31 0.99
70 0.2 0.1 665.35 1.12
70 0.2 0.2 1326.9 1.96
70 0.2 0.25 1651.5 1.73
70 0.25 0.1 822.97 0.99
70 0.25 0.2 1641.19 1.17
70 0.25 0.25 2042.57 0.85
machine-learning python data data-analysis
asked 2 days ago
55thSwiss55thSwiss
63
New contributor
55thSwiss is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
|
show 1 more comment
$begingroup$
I have a table who's inputs (sfm, fr, and doc) all affect the outputs (mmr and ra). How could I go about finding the input importance on the outputs? Basically, I'd like to be able to have a goal output in mmr and ra and have a good idea of starting parameters for sfm, fr, and doc. Does anyone have insight into something like this? Below is a sample of the data.
sfm fr doc mmr ra
60 0.15 0.1 449.6 1.85
60 0.15 0.2 896.78 0.86
60 0.15 0.25 1116.34 1.28
60 0.2 0.1 593.46 1.42
60 0.2 0.2 1183.62 0.91
60 0.2 0.25 1473.34 1.91
60 0.25 0.1 734.26 1.59
60 0.25 0.2 1464.41 1.52
60 0.25 0.25 1822.79 1.07
70 0.15 0.1 503.3 1.42
70 0.15 0.2 1003.74 0.89
70 0.15 0.25 750.31 0.99
70 0.2 0.1 665.35 1.12
70 0.2 0.2 1326.9 1.96
70 0.2 0.25 1651.5 1.73
70 0.25 0.1 822.97 0.99
70 0.25 0.2 1641.19 1.17
70 0.25 0.25 2042.57 0.85
machine-learning python data data-analysis
asked 2 days ago
55thSwiss55thSwiss
63
New contributor
55thSwiss is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
$begingroup$
I think you want a [reverse] prediction from x:(mmr, ra) to y:(sfm, fr, doc)? you what a y for a given x?
$endgroup$
– Esmailian
2 days ago
$begingroup$
Yes, exactly! I'll collect data points in the real world as the basis, but I can't test every possible outcome, so I'd like to know how I can figure out the "ideal" inputs from target outputs based on this collected data
$endgroup$
– 55thSwiss
2 days ago
$begingroup$
This can be done we a standard neural network that has 2 dimensional input for (mmr, a) and 3 dimensional output for (sfm, fr, doc), done! After model is trained, you input an arbitrary (mmr, a) and it gives (sfm, fr, doc). If it works let me know to put it into an answer
$endgroup$
– Esmailian
2 days ago
$begingroup$
Yes, I've been fooling around with that a little, unfortunately it's not yielding great results (and by not great I mean not even in the ballpark). Would you have any suggestion as to the type of neural net? CNN, RNN, LSTM, etc? Or attributes?
$endgroup$
– 55thSwiss
2 days ago
$begingroup$
Did you calculate the correlation between each of the input columns and the outputs?
$endgroup$
– Alireza Zolanvari
yesterday
|
show 1 more comment
$begingroup$
I have a table who's inputs (sfm, fr, and doc) all affect the outputs (mmr and ra). How could I go about finding the input importance on the outputs? Basically, I'd like to be able to have a goal output in mmr and ra and have a good idea of starting parameters for sfm, fr, and doc. Does anyone have insight into something like this? Below is a sample of the data.
sfm fr doc mmr ra
60 0.15 0.1 449.6 1.85
60 0.15 0.2 896.78 0.86
60 0.15 0.25 1116.34 1.28
60 0.2 0.1 593.46 1.42
60 0.2 0.2 1183.62 0.91
60 0.2 0.25 1473.34 1.91
60 0.25 0.1 734.26 1.59
60 0.25 0.2 1464.41 1.52
60 0.25 0.25 1822.79 1.07
70 0.15 0.1 503.3 1.42
70 0.15 0.2 1003.74 0.89
70 0.15 0.25 750.31 0.99
70 0.2 0.1 665.35 1.12
70 0.2 0.2 1326.9 1.96
70 0.2 0.25 1651.5 1.73
70 0.25 0.1 822.97 0.99
70 0.25 0.2 1641.19 1.17
70 0.25 0.25 2042.57 0.85
machine-learning python data data-analysis
asked 2 days ago
55thSwiss55thSwiss
63
New contributor
55thSwiss is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I have a table who's inputs (sfm, fr, and doc) all affect the outputs (mmr and ra). How could I go about finding the input importance on the outputs? Basically, I'd like to be able to have a goal output in mmr and ra and have a good idea of starting parameters for sfm, fr, and doc. Does anyone have insight into something like this? Below is a sample of the data.
sfm fr doc mmr ra
60 0.15 0.1 449.6 1.85
60 0.15 0.2 896.78 0.86
60 0.15 0.25 1116.34 1.28
60 0.2 0.1 593.46 1.42
60 0.2 0.2 1183.62 0.91
60 0.2 0.25 1473.34 1.91
60 0.25 0.1 734.26 1.59
60 0.25 0.2 1464.41 1.52
60 0.25 0.25 1822.79 1.07
70 0.15 0.1 503.3 1.42
70 0.15 0.2 1003.74 0.89
70 0.15 0.25 750.31 0.99
70 0.2 0.1 665.35 1.12
70 0.2 0.2 1326.9 1.96
70 0.2 0.25 1651.5 1.73
70 0.25 0.1 822.97 0.99
70 0.25 0.2 1641.19 1.17
70 0.25 0.25 2042.57 0.85
machine-learning python data data-analysis
machine-learning python data data-analysis
asked 2 days ago
55thSwiss55thSwiss
63
New contributor
55thSwiss is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
55thSwiss55thSwiss
63
New contributor
55thSwiss is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
55thSwiss55thSwiss
63
New contributor
55thSwiss is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
55thSwiss55thSwiss
63
asked 2 days ago
55thSwiss55thSwiss
63
63
New contributor
55thSwiss is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
55thSwiss is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
55thSwiss is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$begingroup$
I think you want a [reverse] prediction from x:(mmr, ra) to y:(sfm, fr, doc)? you what a y for a given x?
$endgroup$
– Esmailian
2 days ago
$begingroup$
Yes, exactly! I'll collect data points in the real world as the basis, but I can't test every possible outcome, so I'd like to know how I can figure out the "ideal" inputs from target outputs based on this collected data
$endgroup$
– 55thSwiss
2 days ago
$begingroup$
This can be done we a standard neural network that has 2 dimensional input for (mmr, a) and 3 dimensional output for (sfm, fr, doc), done! After model is trained, you input an arbitrary (mmr, a) and it gives (sfm, fr, doc). If it works let me know to put it into an answer
$endgroup$
– Esmailian
2 days ago
$begingroup$
Yes, I've been fooling around with that a little, unfortunately it's not yielding great results (and by not great I mean not even in the ballpark). Would you have any suggestion as to the type of neural net? CNN, RNN, LSTM, etc? Or attributes?
$endgroup$
– 55thSwiss
2 days ago
$begingroup$
Did you calculate the correlation between each of the input columns and the outputs?
$endgroup$
– Alireza Zolanvari
yesterday
|
show 1 more comment
$begingroup$
I think you want a [reverse] prediction from x:(mmr, ra) to y:(sfm, fr, doc)? you what a y for a given x?
$endgroup$
– Esmailian
2 days ago
$begingroup$
Yes, exactly! I'll collect data points in the real world as the basis, but I can't test every possible outcome, so I'd like to know how I can figure out the "ideal" inputs from target outputs based on this collected data
$endgroup$
– 55thSwiss
2 days ago
$begingroup$
This can be done we a standard neural network that has 2 dimensional input for (mmr, a) and 3 dimensional output for (sfm, fr, doc), done! After model is trained, you input an arbitrary (mmr, a) and it gives (sfm, fr, doc). If it works let me know to put it into an answer
$endgroup$
– Esmailian
2 days ago
$begingroup$
Yes, I've been fooling around with that a little, unfortunately it's not yielding great results (and by not great I mean not even in the ballpark). Would you have any suggestion as to the type of neural net? CNN, RNN, LSTM, etc? Or attributes?
$endgroup$
– 55thSwiss
2 days ago
$begingroup$
Did you calculate the correlation between each of the input columns and the outputs?
$endgroup$
– Alireza Zolanvari
yesterday
$begingroup$
I think you want a [reverse] prediction from x:(mmr, ra) to y:(sfm, fr, doc)? you what a y for a given x?
$endgroup$
– Esmailian
2 days ago
$begingroup$
I think you want a [reverse] prediction from x:(mmr, ra) to y:(sfm, fr, doc)? you what a y for a given x?
$endgroup$
– Esmailian
2 days ago
$begingroup$
Yes, exactly! I'll collect data points in the real world as the basis, but I can't test every possible outcome, so I'd like to know how I can figure out the "ideal" inputs from target outputs based on this collected data
$endgroup$
– 55thSwiss
2 days ago
$begingroup$
Yes, exactly! I'll collect data points in the real world as the basis, but I can't test every possible outcome, so I'd like to know how I can figure out the "ideal" inputs from target outputs based on this collected data
$endgroup$
– 55thSwiss
2 days ago
$begingroup$
This can be done we a standard neural network that has 2 dimensional input for (mmr, a) and 3 dimensional output for (sfm, fr, doc), done! After model is trained, you input an arbitrary (mmr, a) and it gives (sfm, fr, doc). If it works let me know to put it into an answer
$endgroup$
– Esmailian
2 days ago
$begingroup$
This can be done we a standard neural network that has 2 dimensional input for (mmr, a) and 3 dimensional output for (sfm, fr, doc), done! After model is trained, you input an arbitrary (mmr, a) and it gives (sfm, fr, doc). If it works let me know to put it into an answer
$endgroup$
– Esmailian
2 days ago
$begingroup$
Yes, I've been fooling around with that a little, unfortunately it's not yielding great results (and by not great I mean not even in the ballpark). Would you have any suggestion as to the type of neural net? CNN, RNN, LSTM, etc? Or attributes?
$endgroup$
– 55thSwiss
2 days ago
$begingroup$
Yes, I've been fooling around with that a little, unfortunately it's not yielding great results (and by not great I mean not even in the ballpark). Would you have any suggestion as to the type of neural net? CNN, RNN, LSTM, etc? Or attributes?
$endgroup$
– 55thSwiss
2 days ago
$begingroup$
Did you calculate the correlation between each of the input columns and the outputs?
$endgroup$
– Alireza Zolanvari
yesterday
$begingroup$
Did you calculate the correlation between each of the input columns and the outputs?
$endgroup$
– Alireza Zolanvari
yesterday
|
show 1 more comment
1 Answer
1
active
oldest
votes
$begingroup$
Pearson correlation can be used for this purpose. The Pearson correlation between two entity shows that how mush the values of these two are linearly related to each other.
According to the Cauchy–Schwarz inequality it has a value between +1
and −1, where 1 is total positive linear correlation, 0 is no linear
correlation, and −1 is total negative linear correlation.
According to the values you reported here, there is a strong correlation between doc and mrr so the role of doc in the prediction of mrr is more important than others.
But on the other hand none of the features doesn't have any considerable linear correlation with ra. In this case you can test some other correlation methods.
For further information visit here. It can be helpful to you.
Conclusion: the most important feature in predicting an output is the most correlated one with it which has a considerable correlation.
answered yesterday
Alireza ZolanvariAlireza Zolanvari
19114
$endgroup$
add a comment |
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1 Answer
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active
oldest
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1 Answer
1
active
oldest
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oldest
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active
oldest
votes
$begingroup$
Pearson correlation can be used for this purpose. The Pearson correlation between two entity shows that how mush the values of these two are linearly related to each other.
According to the Cauchy–Schwarz inequality it has a value between +1
and −1, where 1 is total positive linear correlation, 0 is no linear
correlation, and −1 is total negative linear correlation.
According to the values you reported here, there is a strong correlation between doc and mrr so the role of doc in the prediction of mrr is more important than others.
But on the other hand none of the features doesn't have any considerable linear correlation with ra. In this case you can test some other correlation methods.
For further information visit here. It can be helpful to you.
Conclusion: the most important feature in predicting an output is the most correlated one with it which has a considerable correlation.
answered yesterday
Alireza ZolanvariAlireza Zolanvari
19114
$endgroup$
add a comment |
$begingroup$
Pearson correlation can be used for this purpose. The Pearson correlation between two entity shows that how mush the values of these two are linearly related to each other.
According to the Cauchy–Schwarz inequality it has a value between +1
and −1, where 1 is total positive linear correlation, 0 is no linear
correlation, and −1 is total negative linear correlation.
According to the values you reported here, there is a strong correlation between doc and mrr so the role of doc in the prediction of mrr is more important than others.
But on the other hand none of the features doesn't have any considerable linear correlation with ra. In this case you can test some other correlation methods.
For further information visit here. It can be helpful to you.
Conclusion: the most important feature in predicting an output is the most correlated one with it which has a considerable correlation.
answered yesterday
Alireza ZolanvariAlireza Zolanvari
19114
$endgroup$
add a comment |
$begingroup$
Pearson correlation can be used for this purpose. The Pearson correlation between two entity shows that how mush the values of these two are linearly related to each other.
According to the Cauchy–Schwarz inequality it has a value between +1
and −1, where 1 is total positive linear correlation, 0 is no linear
correlation, and −1 is total negative linear correlation.
According to the values you reported here, there is a strong correlation between doc and mrr so the role of doc in the prediction of mrr is more important than others.
But on the other hand none of the features doesn't have any considerable linear correlation with ra. In this case you can test some other correlation methods.
For further information visit here. It can be helpful to you.
Conclusion: the most important feature in predicting an output is the most correlated one with it which has a considerable correlation.
answered yesterday
Alireza ZolanvariAlireza Zolanvari
19114
$endgroup$
Pearson correlation can be used for this purpose. The Pearson correlation between two entity shows that how mush the values of these two are linearly related to each other.
According to the Cauchy–Schwarz inequality it has a value between +1
and −1, where 1 is total positive linear correlation, 0 is no linear
correlation, and −1 is total negative linear correlation.
According to the values you reported here, there is a strong correlation between doc and mrr so the role of doc in the prediction of mrr is more important than others.
But on the other hand none of the features doesn't have any considerable linear correlation with ra. In this case you can test some other correlation methods.
For further information visit here. It can be helpful to you.
Conclusion: the most important feature in predicting an output is the most correlated one with it which has a considerable correlation.
answered yesterday
Alireza ZolanvariAlireza Zolanvari
19114
answered yesterday
Alireza ZolanvariAlireza Zolanvari
19114
answered yesterday
Alireza ZolanvariAlireza Zolanvari
19114
answered yesterday
Alireza ZolanvariAlireza Zolanvari
19114
19114
add a comment |
add a comment |
55thSwiss is a new contributor. Be nice, and check out our Code of Conduct.
55thSwiss is a new contributor. Be nice, and check out our Code of Conduct.
55thSwiss is a new contributor. Be nice, and check out our Code of Conduct.
55thSwiss is a new contributor. Be nice, and check out our Code of Conduct.
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$begingroup$
I think you want a [reverse] prediction from x:(mmr, ra) to y:(sfm, fr, doc)? you what a y for a given x?
$endgroup$
– Esmailian
2 days ago
$begingroup$
Yes, exactly! I'll collect data points in the real world as the basis, but I can't test every possible outcome, so I'd like to know how I can figure out the "ideal" inputs from target outputs based on this collected data
$endgroup$
– 55thSwiss
2 days ago
$begingroup$
This can be done we a standard neural network that has 2 dimensional input for (mmr, a) and 3 dimensional output for (sfm, fr, doc), done! After model is trained, you input an arbitrary (mmr, a) and it gives (sfm, fr, doc). If it works let me know to put it into an answer
$endgroup$
– Esmailian
2 days ago
$begingroup$
Yes, I've been fooling around with that a little, unfortunately it's not yielding great results (and by not great I mean not even in the ballpark). Would you have any suggestion as to the type of neural net? CNN, RNN, LSTM, etc? Or attributes?
$endgroup$
– 55thSwiss
2 days ago
$begingroup$
Did you calculate the correlation between each of the input columns and the outputs?
$endgroup$
– Alireza Zolanvari
yesterday